Sold by: kCloudHubs LLC
Deployed on AWS
AWS Free Tier
This product has charges associated with it for seller support. Apache Spark is an open-source, distributed computing system designed for fast, large-scale data processing, supporting SQL, machine learning, streaming, and graph analytics.
Overview
Apache Spark 4.0.1 on Ubuntu 24.04 with Maintenance Support by kCloudHubs. It is a repackaged open-source solution available through the AWS Marketplace. While Spark itself is open source, enterprise support is available for teams that want expert help running production workloads on AWS.
What Apache Spark Is All About
Apache Spark is a fast, powerful analytics engine built for large-scale data processing. Created at UC Berkeley, Spark is known for its in-memory computing model, which allows it to run jobs much faster than traditional disk-based tools like Hadoop MapReduce. It's widely used for everything from ETL pipelines to real-time streaming and machine learning.
Flexible Deployment Options on AWS
On AWS Marketplace, Spark offers multiple deployment options based on how much control you want. You can run it as a ready-to-use Ubuntu 24.04 AMI with support from kCloudHubs, deploy it in containers using the Bitnami Secure Images Helm chart on Kubernetes, or go fully managed with the Databricks.
Shared Capabilities Across All Options
No matter which option you choose, you get the core Apache Spark engine with support for Java, Scala, Python, R, and SQL. All options handle batch processing, structured streaming, machine learning with MLlib, and Spark SQL. They also integrate seamlessly with AWS services, such as Amazon S3, for scalable storage.
Main Differences to Know
The Ubuntu 24.04 AMI is ideal if you want complete control over the operating system, networking, and security, and is great for custom setups or hybrid environments. The Bitnami Helm chart is designed for Kubernetes users who want quick, secure, and repeatable Spark deployments with easy upgrades. Databricks handles the entire infrastructure, offering auto-scaling clusters, built-in security, governance tools, and collaborative notebooks.
Why Teams Choose These Options
The kCloudHubs AMI is best for teams that want direct OS access with optional enterprise support. Bitnami Helm chart is a strong fit for cloud-native teams focused on containers and DevOps automation. Databricks is the go-to choice for organisations that want the most managed experience with minimal operational overhead.
Highlights
- Runs workloads much faster than disk-based engines like Hadoop MapReduce
- Supports Python, Scala, Java, R, and SQL APIs
- Handles batch jobs, streaming data, machine learning, and graph processing in one platform
- Optimised for AWS Cloud and seamless AWS Marketplace deployment
Highlights
- Executes tasks significantly faster than disk-based engines like Hadoop MapReduce.
- APIs available in Python, Scala, Java, R, and SQL.
- Handles batch, streaming, machine learning, and graph processing in one platform.
Details
Sold by
Categories
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
If you are an AWS Free Tier customer with a free plan, you are eligible to subscribe to this offer. You can use free credits to cover the cost of eligible AWS infrastructure. See AWS Free Tier for more details. If you created an AWS account before July 15th, 2025, and qualify for the Legacy AWS Free Tier, Amazon EC2 charges for Micro instances are free for up to 750 hours per month. See Legacy AWS Free Tier for more details.
Dimension | Cost/hour |
|---|---|
m4.large Recommended | $0.05 |
t3.micro | $0.05 |
t2.micro | $0.001 |
t2.2xlarge | $0.05 |
t2.medium | $0.05 |
t3.medium | $0.05 |
t3.nano | $0.05 |
t3.large | $0.05 |
r4.large | $0.05 |
r3.large | $0.05 |
Vendor refund policy
No refund
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Packaged with latest updates as of Jan/2026.
Additional details
Usage instructions
Connect your instance via SSH, the username is ubuntu. More info on SSH: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AccessingInstancesLinux.html - Use the following command to start the Spark Master container: #sudo su #sudo docker restart $(docker ps -q) #http://localhost:8080
Support
Vendor support
Feel free to reach out anytime. Our support team is available 24x7 for assistance. Email: meha@kcloudhubs.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Similar products
Up-to-date, secure, and ready to deploy on Kubernetes. Apache Spark is a high-performance engine for large-scale computing tasks, such as data processing, machine learning and real-time data streaming. It includes APIs for Java, Python, Scala and R.
This product has charges associated with the provision and deployment of the application and AMI support. Apache Spark is a powerful processing engine built around speed, ease of use, and sophisticated analytics. It was originally developed at UC Berkeley in 2009.
Deequ with Apache Spark Pre-configured Stack provides a ready-to-use environment for data quality verification and validation at scale. This AMI bundles Deequ, Apache Spark, Jupyter Notebook, and PostgreSQL for immediate data quality management without complex setup.
Elasticsearch connector for AWS Glue, with Spark DataSource interface.
For new customers, we recommend using the newer version: https://aws.amazon.com/marketplace/pp?sku=jgxj9mdq1krf0baz03cvpgnv.
Customer reviews
No customer reviews yet
Be the first to review this product . We've partnered with PeerSpot to gather customer feedback. You can share your experience by writing or recording a review, or scheduling a call with a PeerSpot analyst.